Scenario Generation of Renewable Energy Resources Based on Denoising Diffusion Probability Models

As renewable energy sources continue to gain a stronger foothold in active distribution grids, the challenge of managing uncertainty in their integration has grown more pronounced. This paper introduces an approach for generating scenarios of renewable energy based on a denoising probability generation model. By leveraging historical datasets and discerning the error relationship between observed and predicted curves, this method enables the derivation of a probability distribution of prediction errors for renewable energy output curves. This addresses the reliability needs of the power system when dealing with uncertain situations. Various static metrics, alongside the operational results of a 33-node IEEE distribution network, demonstrate the efficacy of this scenario generation approach in producing high-quality scenarios. As a result, it enhances the reliability of distribution grid operations and outcomes.